Data Preparation

There isn’t a lot of data preparation to be done before running the demultiplexing or doublet detecting softwares.

Data Required

The demultiplexing and transcriptome-based doublet detecting softwares have different data input requirements:

Software Group

Single Cell Count Data Required

SNP Genotype Data Required

Demultiplexing

✔️

✔️

Doublet Detecting

✔️

✖️

Note

The SNP genotype data can be for multiplexed donors in the pool OR it can be publicly available common SNP genotypes which can be downloaded from 1000G (hg19 or hg38) or from HRC (hg19).

There isn’t any pre-processing that’s needed for the single cell count data. For the demultiplexing softwares, you should filter the SNP genotypes that you will use.

SNP Genotype Pre-processing

It is best to filter the SNP genotypes for common SNPs (generally > 1% or > 5% minor allele frequency) that overlap exons. Here we provide an example of how to do this filtering. We built the required softwares into the singularity image so you can run these filtering steps with the image.

Note

We have found it best to impute reference SNP genotypes so there are more SNP locations available. If you are using reference SNP genotypes for the donors in your pool, please be sure to impute before filtering.

Filter for Common SNPs

First, filter the SNP genotypes for common SNPs - here we use 5% minor allele frequency.

singularity exec Demuxafy.sif bcftools filter --include 'MAF>=0.05' -Oz --output $OUTDIR/common_maf0.05.vcf.gz $VCF

Where $OUTDIR is the output directory where you want to save the results and $VCF is the path to the SNP genotype vcf file.

Filter for SNPs overlapping Exons

Next, filter for the SNPs that overlap exons.

Note

You can get an exon bed using the UCSC table browser (see instructions here) and we have also provided bed files for hg19 and hg38

singularity exec Demuxafy.sif vcftools \
  --gzvcf $OUTDIR/common_maf0.05.vcf.gz \
  --max-alleles 2 \
  --remove-indels \
  --bed $BED \
  --recode \
  --recode-INFO-all \
  --out $OUTDIR/common_maf0.05_exon_filtered

Test Dataset

In addition, we have provided test data that you can use.

Information

The test dataset includes 20,982 droplets captured of PBMCs from 13 multiplexed individuals.

10x Directories + Other Necessary Files

We have provided this dataset as the complete dataset which is pretty large (~40Gb tar.gz directory). Therefore, we have also provided the same dataset where the data has been significantly reduced.

Warning

The reduced test dataset may not produce real-world results due to the small size - especially for doublet detecting softwares since the reads have been significantly downsampled to reduce the size.

You can download the dataset with one of the following commands:

First, download the dataset and the md5sum:

wget https://www.dropbox.com/s/3oujqq98y400rzz/TestData4PipelineFull.tar.gz
wget https://www.dropbox.com/s/5n7u723okkf5m3l/TestData4PipelineFull.tar.gz.md5

After downloading the tar.gz directory, it is best to make sure the md5sum of the TestData4PipelineFull.tar.gz file matches the md5sum in the TestData4PipelineFull.tar.gz.md5:

md5sum TestData4PipelineFull.tar.gz > downloaded_TestData4PipelineFull.tar.gz.md5
diff -s TestData4PipelineFull.tar.gz.md5 downloaded_TestData4PipelineFull.tar.gz.md5

That should return the following statement indicating that the two md5sums are identical:

Files TestData4PipelineFull.tar.gz.md5 and downloaded_TestData4PipelineFull.tar.gz.md5 are identical

Seurat Object

We have also provided a filtered, QC normalized Seurat object (needed for DoubletFinder and DoubletDecon)

Download the rds object and the md5sum:

wget https://www.dropbox.com/s/po4gy2j3eqohhjv/TestData_Seurat.rds
wget https://www.dropbox.com/s/rmix7tt9aw28n7i/TestData_Seurat.rds.md5

After downloading the rds.object, it is best to make sure the md5sum of the TestData_Seurat.rds file matches the md5sum in the TestData_Seurat.rds.md5:

md5sum TestData_Seurat.rds > downloaded_TestData_Seurat.rds.md5
diff -s TestData_Seurat.rds.md5 downloaded_TestData_Seurat.rds.md5

That should return the following statement indicating that the two md5sums are identical:

Files TestData_Seurat.rds.md5 and downloaded_TestData_Seurat.rds.md5 are identical

Note

We have used this dataset for each of the tutorials. The example tables in the Results and Interpretation sections of each tutorial are the results from this dataset.